Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies

Haohan Wang, Tianwei Yue, Jingkang Yang, Wei Wu, Eric P. Xing

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Genome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be modeled and discovered more thoroughly. Results: In this paper, following the previous study of detecting marginal epistasis signals, and motivated by the universal approximation power of deep learning, we propose a neural network method that can potentially model arbitrary interactions between SNPs in genetic association studies as an extension to the mixed models in correcting confounding factors. Our method, namely Deep Mixed Model, consists of two components: 1) a confounding factor correction component, which is a large-kernel convolution neural network that focuses on calibrating the residual phenotypes by removing factors such as population stratification, and 2) a fixed-effect estimation component, which mainly consists of an Long-short Term Memory (LSTM) model that estimates the association effect size of SNPs with the residual phenotype. Conclusions: After validating the performance of our method using simulation experiments, we further apply it to Alzheimer's disease data sets. Our results help gain some explorative understandings of the genetic architecture of Alzheimer's disease.

Original languageEnglish (US)
Article number656
JournalBMC bioinformatics
Volume20
DOIs
StatePublished - Dec 27 2019
Externally publishedYes

Keywords

  • Deep learning
  • GWAS
  • Marginal epistasis
  • Mixed model

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies'. Together they form a unique fingerprint.

Cite this